This review summarizes the current state of brain-computer interface (BCI) paradigms and neural coding. BCI paradigms are designed to represent user intentions through specific mental tasks or external stimuli, which are then decoded into brain signals. Neural coding refers to the process of encoding user intentions into brain signals, which can be decoded by BCI systems. The review discusses the definition and design principles of BCI paradigms, the mechanisms of BCI neural coding, and the relationship between BCI paradigms, neural coding, and neural decoding. It also explores the relationship between BCI neural coding, brain neural coding, and computer information coding. The review highlights various BCI paradigms, including those based on brain imaging techniques such as intracortical local field potentials (LFP), electrocorticography (ECoG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG). The review also discusses the challenges and future research directions of BCI paradigms and neural coding, including user-centered design, revolutionizing traditional BCI paradigms, and breaking through existing brain signal collection techniques to improve decoding performance. The review emphasizes the importance of combining BCI technology with advanced AI to enhance brain signal decoding performance and improve the usability and effectiveness of BCI systems. The review concludes that the development of BCI paradigms and neural coding is essential for the future of BCI technology.This review summarizes the current state of brain-computer interface (BCI) paradigms and neural coding. BCI paradigms are designed to represent user intentions through specific mental tasks or external stimuli, which are then decoded into brain signals. Neural coding refers to the process of encoding user intentions into brain signals, which can be decoded by BCI systems. The review discusses the definition and design principles of BCI paradigms, the mechanisms of BCI neural coding, and the relationship between BCI paradigms, neural coding, and neural decoding. It also explores the relationship between BCI neural coding, brain neural coding, and computer information coding. The review highlights various BCI paradigms, including those based on brain imaging techniques such as intracortical local field potentials (LFP), electrocorticography (ECoG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG). The review also discusses the challenges and future research directions of BCI paradigms and neural coding, including user-centered design, revolutionizing traditional BCI paradigms, and breaking through existing brain signal collection techniques to improve decoding performance. The review emphasizes the importance of combining BCI technology with advanced AI to enhance brain signal decoding performance and improve the usability and effectiveness of BCI systems. The review concludes that the development of BCI paradigms and neural coding is essential for the future of BCI technology.